Modern operations generate vast amounts of distributed data, necessitating a shift from centralized to distributed historian architectures to manage the increased volume and distribution efficiently. Centralized historians are becoming bottlenecks due to their inability to handle the influx of time series data from diverse sources such as factory floors and pipelines, leading to increased costs, slower response times, and limited flexibility. In contrast, distributed historian architectures utilize local historians at the edge to capture and process data near its source, sharing only essential information upstream, which enhances visibility, reduces costs, and improves response times. InfluxDB 3 serves as a modern historian capable of functioning both locally at the edge and centrally, integrating edge deployment patterns, data aggregation strategies, and intelligent filtering techniques to create a resilient, efficient system that supports real-time operations and advanced analytics. This architecture enables organizations to maintain operations during connectivity failures, consolidate data for enterprise-wide visibility, and ensure that only relevant data is communicated to central systems, thereby optimizing bandwidth and improving data quality for analytics and AI. By adopting InfluxDB 3, companies can modernize their data management strategies, overcoming the limitations of centralized systems and preparing for the demands of distributed, data-intensive environments.